Effective management and oversight are crucial for the success and reliability of AI systems.
In this section, you will learn about the continuous monitoring and validation of AI systems, ensuring
these systems operate as intended and remain aligned with their design objectives.
Exploring post-hoc testing for AI system accuracy and effectiveness, you will delve into methodologies
that assess performance and identify potential shortcomings after deployment.
Understanding the complexities of managing automation bias in AI systems is essential, as this lesson
will guide you on recognizing and mitigating unintended biases that could influence decision making
processes.
Model versioning and updates are vital for maintaining the relevance and functionality of AI applications.
Best practices in this domain will be covered, providing you with strategies to efficiently manage
different versions and updates of AI models.
The challenge of managing third party risks post-deployment will also be addressed, offering insights
into how to safeguard your AI systems from vulnerabilities introduced by external vendors or partners.
Reducing unintended use and downstream harm in AI systems is another critical topic where you will learn
to anticipate and prevent adverse consequences that may arise from the misuse of AI technologies.
Finally, planning for AI system deactivation and system sunset will be discussed.
This lesson will equip you with the methodologies to responsibly phase out AI systems, ensuring a smooth
transition and minimal disruption.
By the end of this section, you will gain a comprehensive understanding of these key aspects, empowering
you to effectively oversee and maintain the integrity, performance and ethical standards of AI systems
throughout their life cycle.